The most common technique for learning dynamics is to fit either the forward model, \(s_{t+1}=f_{\theta }(s_t, a_t)\) , or the inverse-model, \(a_t = f^{-1}_{\theta }(s_{t+1}, s_t)\)  to the training samples of \(N\) -step state-action trajectories, \(\tau =\lbrace s_t, a_t\rbrace _{t=1}^N\)  [1]}, [2]}, [3]}, [4]}, [5]}.
